When Inference Becomes Secret Sauce
Fireworks AI customer at Hebbia on serving state-of-the-art models with unified APIs
The break point is not scale alone, it is when inference becomes part of the product’s secret sauce instead of a utility. Hebbia used Fireworks because it needed fast access to new open models, OpenAI style APIs, rate limiting, observability, and autoscaling across chat and batch document jobs. That works well as long as the team is consuming models mostly as they are. The case for raw GPUs appears when the team wants to shape the model itself or tightly control scheduling for bursty workloads.
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Hebbia did not need that deeper control. It used Fireworks for inference only, not fine tuning, and chose it because new models could be added quickly through the same API surface and monitored with token and latency tooling.
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Fireworks is built to sell convenience and performance above raw infrastructure. It offers serverless and dedicated deployments, bring your own model support, and multi LoRA fine tuning, so it can stretch further upmarket before a customer has to leave.
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Voltage Park sits on the other side of that line. It offers bare metal clusters, InfiniBand networking, Slurm, Kubernetes, and direct hardware access, which matters when a team wants to orchestrate GPUs itself and squeeze utilization across large custom workloads.
The likely direction is convergence. Managed inference platforms are moving toward more workflow control and customization, while GPU clouds are adding managed layers. For many enterprise AI apps, managed inference should stay in place long term. Teams only graduate to raw infrastructure when model training, post training, or cluster level scheduling becomes central to product performance and economics.